OpenRaiser/Intern-Atlas huggingface dataset:
Model the System from Adversary Viewpoint: Threats Identification and Modeling
Venue: arXiv cs.CR 2014
Year: 2014

Security attacks are hard to understand, often expressed with unfriendly and limited details, making it difficult for security experts and for security analysts to create intelligible security specifications. For instance, to explain Why (attack objective), What (i.e., system assets, goals, etc.), and How (attack method), adversary achieved his attack goals. We introduce in this paper a security attack meta-model for our SysML-Sec framework, developed to improve the threat identification and modeling through the explicit representation of security concerns with knowledge representation techniques. Our proposed meta-model enables the specification of these concerns through ontological concepts which define the semantics of the security artifacts and introduced using SysML-Sec diagrams. This meta-model also enables representing the relationships that tie several such concepts together. This representation is then used for reasoning about the knowledge introduced by system designers as well as security experts through the graphical environment of the SysML-Sec framework.
The mobile devices and its mobile learning usage analysis
Venue: arXiv cs.CY 2014
Year: 2014

The usage of mobile devices for mobile learning is becoming increasingly popular. There is a new brand of students in the universities now-a-days who are easily connected to technology and innovative mobile devices. We attempt to do an analysis on a survey done with university students on mobile device usage for mobile learning purposes. This is to find the learning trends within the student community so that some of these popular practices could be encouraged to enhance learning among the student community. Both the quantitative and qualitative approaches are adopted in the analysis. The results are discussed and conclusions drawn in the end.
The HAWKwood Database
Venue: arXiv cs.CV 2014
Year: 2014

We present a database consisting of wood pile images, which can be used as a benchmark to evaluate the performance of wood pile detection and surveying algorithms. We distinguish six database cate- gories which can be used for different types of algorithms. Images of real and synthetic scenes are provided, which consist of 7655 images divided into 354 data sets. Depending on the category the data sets either include ground truth data or forestry specific measurements with which algorithms may be compared.
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization
Venue: arXiv cs.CV 2014
Year: 2014

In the recent past, automatic selection or combination of kernels (or features) based on multiple kernel learning (MKL) approaches has been receiving significant attention from various research communities. Though MKL has been extensively studied in the context of support vector machines (SVM), it is relatively less explored for ratio-trace problems. In this paper, we show that MKL can be formulated as a convex optimization problem for a general class of ratio-trace problems that encompasses many popular algorithms used in various computer vision applications. We also provide an optimization procedure that is guaranteed to converge to the global optimum of the proposed optimization problem. We experimentally demonstrate that the proposed MKL approach, which we refer to as MKL-RT, can be successfully used to select features for discriminative dimensionality reduction and cross-modal retrieval. We also show that the proposed convex MKL-RT approach performs better than the recently proposed non-convex MKL-DR approach.
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension
Venue: arXiv cs.LG 2014
Year: 2014

The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better. It is well known that the VC dimension of SVMs can be very large or unbounded, even though they generally yield state-of-the-art learning performance. In this paper, we show how to learn a hyperplane regressor by minimizing an exact, or \boldmath{$Θ$} bound on its VC dimension. The proposed approach, termed as the Minimal Complexity Machine (MCM) Regressor, involves solving a simple linear programming problem. Experimental results show, that on a number of benchmark datasets, the proposed approach yields regressors with error rates much less than those obtained with conventional SVM regresssors, while often using fewer support vectors. On some benchmark datasets, the number of support vectors is less than one tenth the number used by SVMs, indicating that the MCM does indeed learn simpler representations.
Analysis of Phishing Attacks and Countermeasures
Venue: arXiv cs.CR 2014
Year: 2014

One of the biggest problems with the Internet technology is the unwanted spam emails. The well disguised phishing email comes in as part of the spam and makes its entry into the inbox quite frequently nowadays. While phishing is normally considered a consumer issue, the fraudulent tactics the phishers use are now intimidating the corporate sector as well. In this paper, we analyze the various aspects of phishing attacks and draw on some possible defenses as countermeasures. We initially address the different forms of phishing attacks in theory, and then look at some examples of attacks in practice, along with their common defenses. We also highlight some recent statistical data on phishing scam to project the seriousness of the problem. Finally, some specific phishing countermeasures at both the user level and the organization level are listed, and a multi-layered anti-phishing proposal is presented to round up our studies.
Weighted decomposition in high-performance lattice-Boltzmann simulations: are some lattice sites more equal than others?
Venue: arXiv cs.DC 2014
Year: 2014

Obtaining a good load balance is a significant challenge in scaling up lattice-Boltzmann simulations of realistic sparse problems to the exascale. Here we analyze the effect of weighted decomposition on the performance of the HemeLB lattice-Boltzmann simulation environment, when applied to sparse domains. Prior to domain decomposition, we assign wall and in/outlet sites with increased weights which reflect their increased computational cost. We combine our weighted decomposition with a second optimization, which is to sort the lattice sites according to a space filling curve. We tested these strategies on a sparse bifurcation and very sparse aneurysm geometry, and find that using weights reduces calculation load imbalance by up to 85%, although the overall communication overhead is higher than some of our runs.
Influence of Information Support System on ICT Use by distance learners in University of Lagos Nigeria
Venue: arXiv cs.CY 2014
Year: 2014

The use of modern technology in Education is the key to an increased drive for learning which shape learners critical and analytic competencies with respect to disciplinary knowledge. Distance education (DE) is a system of learning driven by computer linked to internet. The flexible nature of DE avail students who are unable to attend full time education due to age, social or religious barriers. However, in Nigeria, the University of Lagos distance learning Institute has its shortfalls traced to poor student support system, which affects the service delivery to students. The study examined Influence of Information Support on ICT use by distance learners.
Redesigning Commercial Floating-Gate Memory for Analog Computing Applications
Venue: arXiv cs.ET 2014
Year: 2014

We have modified a commercial NOR flash memory array to enable high-precision tuning of individual floating-gate cells for analog computing applications. The modified array area per cell in a 180 nm process is about 1.5 um^2. While this area is approximately twice the original cell size, it is still at least an order of magnitude smaller than in the state-of-the-art analog circuit implementations. The new memory cell arrays have been successfully tested, in particular confirming that each cell may be automatically tuned, with ~1% precision, to any desired subthreshold readout current value within an almost three-orders-of-magnitude dynamic range, even using an unoptimized tuning algorithm. Preliminary results for a four-quadrant vector-by-matrix multiplier, implemented with the modified memory array gate-coupled with additional peripheral floating-gate transistors, show highly linear transfer characteristics over a broad range of input currents.
A Modality Lexicon and its use in Automatic Tagging
Venue: arXiv cs.CL 2014
Year: 2014

This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger---a structure-based tagger---results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.
Generalized Summation-by-Parts Operators for the Second Derivative with Variable Coefficients
Venue: arXiv cs.NA 2014
Year: 2014

The comprehensive generalization of summation-by-parts of Del Rey Fernández et al.\ (J. Comput. Phys., 266, 2014) is extended to approximations of second derivatives with variable coefficients. This enables the construction of second-derivative operators with one or more of the following characteristics: i) non-repeating interior stencil, ii) nonuniform nodal distributions, and iii) exclusion of one or both boundary nodes. Definitions are proposed that give rise to generalized SBP operators that result in consistent, conservative, and stable discretizations of PDEs with or without mixed derivatives. It is proven that such operators can be constructed using a correction to the application of the first-derivative operator twice that is the same as used for the constant-coefficient operator. Moreover, for operators with a repeating interior stencil, a decomposition is proposed that makes the application of such operators particularly simple. A number of novel operators are constructed, including operators on pseudo-spectral nodal distributions and operators that have a repeating interior stencil, but unequal nodal spacing near boundaries. The various operators are compared to the application of the first-derivative operator twice in the context of the linear convection-diffusion equation with constant and variable coefficients.
On the Capacity Regions of Two-Way Diamond Channels
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study the capacity regions of two-way diamond channels. We show that for a linear deterministic model the capacity of the diamond channel in each direction can be simultaneously achieved for all values of channel parameters, where the forward and backward channel parameters are not necessarily the same. We divide the achievability scheme into three cases, depending on the forward and backward channel parameters. For the first case, we use a reverse amplify-and-forward strategy in the relays. For the second case, we use four relay strategies based on the reverse amplify-and-forward with some modifications in terms of replacement and repetition of some stream levels. For the third case, we use two relay strategies based on performing two rounds of repetitions in a relay. The proposed schemes for deterministic channels are used to find the capacity regions within constant gaps for two special cases of the Gaussian two-way diamond channel. First, for the general Gaussian two-way relay channel with a simple coding scheme the smallest gap is achieved compared to the prior works. Then, a special symmetric Gaussian two-way diamond model is considered and the capacity region is achieved within four bits.
Frames for subspaces of $\mathbb{C}^N$
Venue: arXiv cs.IT 2014
Year: 2014

We present a theory of finite frames for subspaces of $\mathbb{C}^N$ . The definition of a subspace frame is given and results analogous to those from frame theory for $\mathbb{C}^N$ are proven.
An Overview of General Performance Metrics of Binary Classifier Systems
Venue: arXiv cs.LG 2014
Year: 2014

This document provides a brief overview of different metrics and terminology that is used to measure the performance of binary classification systems.
Machine Learning of Coq Proof Guidance: First Experiments
Venue: arXiv cs.LO 2014
Year: 2014

We report the results of the first experiments with learning proof dependencies from the formalizations done with the Coq system. We explain the process of obtaining the dependencies from the Coq proofs, the characterization of formulas that is used for the learning, and the evaluation method. Various machine learning methods are compared on a dataset of 5021 toplevel Coq proofs coming from the CoRN repository. The best resulting method covers on average 75% of the needed proof dependencies among the first 100 predictions, which is a comparable performance of such initial experiments on other large-theory corpora.
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Venue: arXiv cs.CL 2014
Year: 2014

Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield.
On the feasibility of beamforming in millimeter wave communication systems with multiple antenna arrays
Venue: arXiv cs.IT 2014
Year: 2014

The use of the millimeter (mm) wave spectrum for next generation (5G) mobile communication has gained significant attention recently. The small carrier wavelengths at mmwave frequencies enable synthesis of compact antenna arrays, providing beamforming gains that compensate the increased propagation losses. In this work, we investigate the feasibility of employing multiple antenna arrays to obtain diversity/multiplexing gains in mmwave systems, where each of the arrays is capable of beamforming independently. Considering a codebook based beamforming system (e.g., to facilitate limited feedback), we observe that the complexity of \emph{jointly} optimizing the beamforming directions across the multiple arrays is highly prohibitive, even for very reasonable system parameters. To overcome this bottleneck, we develop reduced complexity algorithms for optimizing the choice of beamforming directions, premised on the sparse multipath structure of the mmwave channel. Specifically, we reduce the cardinality of the joint beamforming search space, by restricting attention to a small set of dominant candidate directions. To obtain the set of dominant directions, we develop two complementary approaches: (a) based on computation of a novel spatial power metric; a detailed analysis of this metric shows that, in the limit of large antenna arrays, the selected candidate directions approach the channel's dominant angles of arrival and departure, and (b) precise estimation of the channel's (long-term) dominant angles of arrival, exploiting the correlations of the signals received across the different receiver subarrays. Our methods enable a drastic reduction of the optimization search space (a factor of 100 reduction), while delivering close to optimal performance, thereby indicating the potential feasibility of achieving diversity and multiplexing gains in mmwave systems.
Towards energy efficiency and maximum computational intensity for stencil algorithms using wavefront diamond temporal blocking
Venue: arXiv cs.PF 2014
Year: 2014

We study the impact of tunable parameters on computational intensity (i.e., inverse code balance) and energy consumption of multicore-optimized wavefront diamond temporal blocking (MWD) applied to different stencil-based update schemes. MWD combines the concepts of diamond tiling and multicore-aware wavefront blocking in order to achieve lower cache size requirements than standard single-core wavefront temporal blocking. We analyze the impact of the cache block size on the theoretical and observed code balance, introduce loop tiling in the leading dimension to widen the range of applicable diamond sizes, and show performance results on a contemporary Intel CPU. The impact of code balance on power dissipation on the CPU and in the DRAM is investigated and shows that DRAM power is a decisive factor for energy consumption, which is strongly influenced by the code balance. Furthermore we show that highest performance does not necessarily lead to lowest energy even if the clock speed is fixed.
Polynomials: a new tool for length reduction in binary discrete convolutions
Venue: arXiv cs.DS 2014
Year: 2014

Efficient handling of sparse data is a key challenge in Computer Science. Binary convolutions, such as polynomial multiplication or the Walsh Transform are a useful tool in many applications and are efficiently solved. In the last decade, several problems required efficient solution of sparse binary convolutions. both randomized and deterministic algorithms were developed for efficiently computing the sparse polynomial multiplication. The key operation in all these algorithms was length reduction. The sparse data is mapped into small vectors that preserve the convolution result. The reduction method used to-date was the modulo function since it preserves location (of the "1" bits) up to cyclic shift. To date there is no known efficient algorithm for computing the sparse Walsh transform. Since the modulo function does not preserve the Walsh transform a new method for length reduction is needed. In this paper we present such a new method - polynomials. This method enables the development of an efficient algorithm for computing the binary sparse Walsh transform. To our knowledge, this is the first such algorithm. We also show that this method allows a faster deterministic computation of sparse polynomial multiplication than currently known in the literature.
The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services
Venue: arXiv cs.SE 2014
Year: 2014

This work aims to resolve issues related to Web Service retrieval, also known as Service Selection, Discovery or essentially Matching, in two directions. Firstly, a novel matching algorithm for SAWSDL is introduced. The algorithm is hybrid in nature, combining novel and known concepts, such as a logic-based strategy and syntactic text-similarity measures on semantic annotations and textual descriptions. A plugin for the S3 contest environment was developed, in order to position Tomaco amongst state-of-the-art in an objective, reproducible manner. Evaluation showed that Tomaco ranks high amongst state of the art, especially for early recall levels. Secondly, this work introduces the Tomaco web application, which aims to accelerate the wide-spread adoption of Semantic Web Service technologies and algorithms while targeting the lack of user-friendly applications in this field. Tomaco integrates a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.
Divide and Conquer: Partitioning OSPF networks with SDN
Venue: arXiv cs.NI 2014
Year: 2014

Software Defined Networking (SDN) is an emerging network control paradigm focused on logical centralization and programmability. At the same time, distributed routing protocols, most notably OSPF and IS-IS, are still prevalent in IP networks, as they provide shortest path routing, fast topological convergence after network failures, and, perhaps most importantly, the confidence based on decades of reliable operation. Therefore, a hybrid SDN/OSPF operation remains a desirable proposition. In this paper, we propose a new method of hybrid SDN/OSPF operation. Our method is different from other hybrid approaches, as it uses SDN nodes to partition an OSPF domain into sub-domains thereby achieving the traffic engineering capabilities comparable to full SDN operation. We place SDN-enabled routers as sub-domain border nodes, while the operation of the OSPF protocol continues unaffected. In this way, the SDN controller can tune routing protocol updates for traffic engineering purposes before they are flooded into sub-domains. While local routing inside sub-domains remains stable at all times, inter-sub-domain routes can be optimized by determining the routes in each traversed sub-domain. As the majority of traffic in non-trivial topologies has to traverse multiple sub-domains, our simulation results confirm that a few SDN nodes allow traffic engineering up to a degree that renders full SDN deployment unnecessary.
Investigation of A Collective Decision Making System of Different Neighbourhood-Size Based on Hyper-Geometric Distribution
Venue: arXiv cs.AI 2014
Year: 2014

The study of collective decision making system has become the central part of the Swarm- Intelligence Related research in recent years. The most challenging task of modelling a collec- tive decision making system is to develop the macroscopic stochastic equation from its microscopic model. In this report we have investigated the behaviour of a collective decision making system with specified microscopic rules that resemble the chemical reaction and used different group size. Then we ventured to derive a generalized analytical model of a collective-decision system using hyper-geometric distribution. Index Terms-swarm; collective decision making; noise; group size; hyper-geometric distribution
Compositional Structure Learning for Action Understanding
Venue: arXiv cs.CV 2014
Year: 2014

The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile robotics and video search, with solutions to classification, localization and detection. In this paper, we propose a compositional model that leverages a new mid-level representation called compositional trajectories and a locally articulated spatiotemporal deformable parts model (LALSDPM) for fully action understanding. Our methods is advantageous in capturing the variable structure of dynamic human activity over a long range. First, the compositional trajectories capture long-ranging, frequently co-occurring groups of trajectories in space time and represent them in discriminative hierarchies, where human motion is largely separated from camera motion; second, LASTDPM learns a structured model with multi-layer deformable parts to capture multiple levels of articulated motion. We implement our methods and demonstrate state of the art performance on all three problems: action detection, localization, and recognition.